基于IMERG反演降水数据估算广东省降雨侵蚀力

近年来遥感反演降水产品的时空分辨率不断提高，为估算区域尺度上具有空间连续性的降雨侵蚀力提供了新的可能。但以往研究在应用遥感降水产品估算降雨侵蚀力时多忽略了其与站点观测数据间的差异和对其纠偏的可能性。该研究以广东省86个气象站2001—2020年的逐时降水资料估算的降雨侵蚀力为观测值，评估两套IMERG（integrated multi-satellite retrievals for GPM）遥感降水产品-GPM_3IMERGHH（0.1°，逐30-min）和GPM_3IMERGDF（0.1°，逐日）对广东省降雨侵蚀力的估算精度并量化偏差，再结合拟合纠偏确定基于遥感反演降水数据估算广东省降雨侵蚀力的最优方法。结果表明：这两套产品均不适宜直接估算降雨侵蚀力指标，不同时间尺度、不同方法直接应用时精度均较低，克林-古普塔效率系数（Kling-Gupta efficiency, KGE）小于等于0.51。但多年平均和极端次事件降雨侵蚀力与对应观测值间具有强相关性（皮尔逊相关系数大于等于0.78），具备纠偏的潜力。因此，本研究发展线性模型对IMERG估算结果进行纠偏，交叉验证结果表明纠偏后GPM_3IMERGHH估算多年平均降雨侵蚀力（R因子）的KGE可达0.79，10年一遇EI₃₀的KGE可达0.64，优于采用站点日降水估算降雨侵蚀力并插值的精度（KGE分别为0.60和0.59），与采用站点小时降水估算降雨侵蚀力并插值的精度相近（KGE分别为0.77和0.66）。当前研究结果充分展示了遥感反演降水在土壤水蚀领域的应用潜力和前景。

Estimating rainfall erosivity in Guangdong Province using IMERG remote sensing precipitation products

Spatiotemporal resolution of remote sensing precipitation products has been continuously improved in recent years. New possibilities can be provided to estimate the spatially continuous rainfall erosivity at the regional scale. However, previous studies have often failed to consider the differences between these products and observation data, as well as the potential for bias correction. In this study, the remote sensing precipitation products were used to estimate the rainfall erosivity in Guangdong Province, China. The precipitation data was collected hourly from 86 meteorological stations from 2001 to 2020. Two IMERG remote sensing precipitation products were selected, including the GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06 (GPM_3IMERGHH, 0.1°, every 30-min) and GPM IMERG Final Precipitation L3 1 day 0.1 degree × 0.1 degree V06 (GPM_3IMERGDF, 0.1°, daily). Extreme event scales were then evaluated to quantify the bias at the semi-monthly, annual, and multi-year averages. Fitting and bias correction were utilized to determine the optimal rainfall erosivity using remote sensing precipitation data. The EI₃₀ was an indicative measure of event rainfall erosivity derived from hourly rainfall data. Fitting revisions of the EI₃₀ were estimated from 5-minute rainfall data collected from three meteorological stations. The results showed that neither product was suitable for the direct estimation of rainfall erosivity with low accuracy at different time scales. Pearson correlation coefficients (CC) and Kling-Gupta efficiency (KGE) were less than 0.63 and 0.51 on a semi-monthly scale, indicating low estimation accuracy and small correlation. The KGE values were less than 0.5 on an annual scale, whereas, the CC values were higher than those on a semi-monthly scale, all exceeding 0.63. The KGE values were still less than 0.5 on the multi-year average and extreme event rainfall erosivity. But the CC was greater than or equal to 0.78. There was a strong correlation between the estimated and the observed values, indicating the potential for bias correction. Therefore, the linear models were used to correct the IMERG estimates. Among them, the correction models were y=2.0017x-1373 and y=1.6916x-4, respectively, for the multi-year average annual rainfall erosivity (R-factor) and 10-year storm EI30 (EI30 that occurs once in 10 years). Cross-validation results showed that the corrected GPM_3IMERGHH estimates of R-factor and 10-year storm EI₃₀ had the KGE values of 0.79 and 0.64, respectively, which were superior to the spatial interpolation using station daily rainfall data (KGE values of 0.60 and 0.59, respectively), and similar to the station hourly rainfall data (KGE values of 0.77 and 0.66, respectively). R-factor and 10-year storm EI₃₀ were necessary to require for the areas of Guangdong Province without observation stations, in order to map the spatial distribution of these factors. Specifically, IMERG remote sensing precipitation products were used to estimate the rainfall erosivity with the bias corrections. The accuracy of the corrected estimates was superior to that using spatial interpolation with the station daily rainfall data, and similar to that with the station hourly one. The findings can effectively demonstrate the substantial potential of the remote sensing precipitation products in the field of soil water erosion, indicating the broad range of future possibilities.